{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import time\n",
    "import tarfile\n",
    "import shutil\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import ascii\n",
    "from astropy import constants as cons\n",
    "from astropy.table import Table, Column, vstack, join\n",
    "import astropy.coordinates as coords\n",
    "from astropy import units as u\n",
    "from scipy import stats\n",
    "import matplotlib.mlab as mlab\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rc(\"font\", family=\"serif\", size=16, weight=2)\n",
    "mpl.rc(\"axes\", linewidth = 2.0)\n",
    "mpl.rc(\"lines\", linewidth = 1.0)\n",
    "mpl.rc(\"xtick.major\", pad = 5, width = 1,size=8)\n",
    "mpl.rc(\"ytick.major\", pad = 5, width = 1,size=8)\n",
    "mpl.rc(\"xtick.minor\", width = 1,size=4)\n",
    "mpl.rc(\"ytick.minor\", width = 1,size=4)\n",
    "mpl.rc('text', usetex=False)\n",
    "mpl.rcParams['xtick.direction'] = 'in'\n",
    "mpl.rcParams['ytick.direction'] = 'in'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sedlist = ascii.read('../N109_clumps/TableGet/aaa.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as mtick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize = (8,8))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.set_xscale('log')\n",
    "ax1.set_yscale('log')\n",
    "ax1.set_xlim(0.2,1.2)\n",
    "ax1.set_ylim(50,4000)\n",
    "# 设置y刻度：用文字来显示刻度\n",
    "plt.yticks([100, 600, 1000, 2000, 3000])\n",
    "plt.xticks([])\n",
    "new_ticks = np.linspace(0.2, 1, 5)\n",
    "plt.xticks(new_ticks)\n",
    "ax1.xaxis.set_major_formatter(mtick.FormatStrFormatter(\"%.1f\"))\n",
    "ax1.yaxis.set_major_formatter(mtick.FormatStrFormatter(\"%d\"))\n",
    "x = np.logspace(np.log10(0.002), np.log10(30), 1000)\n",
    "#x = np.linspace(0.2,1,1000)\n",
    "y = 870.0*x**1.33\n",
    "ax1.plot(x,y,'r--',zorder = 1, linewidth = 1)\n",
    "for i in [14,42,51]:\n",
    "    ax1.scatter(sedlist['r_pc'][i],sedlist['mass'][i], marker = 's',s=50,  \n",
    "                    color = 'w',edgecolors=\"black\")\n",
    "\"\"\"for i in range(len(sedlist)):\n",
    "    if (sedlist[\"Ma\"][i]/sedlist['mass'][i])<=2:       \n",
    "        ax1.scatter(sedlist['r_pc'][i],sedlist['mass'][i], marker = '.',s=60,  \n",
    "                    color = 'blue')\n",
    "    else:\n",
    "        ax1.scatter(sedlist['r_pc'][i],sedlist['mass'][i], marker = '.',s=60, \n",
    "                    color = 'red')  \n",
    "\"\"\" \n",
    "for i in range(len(sedlist)):      \n",
    "    ax1.scatter(sedlist['r_pc'][i],sedlist['mass'][i], marker = '.',s=60,  \n",
    "                    color = 'blue')\n",
    "#ax1.errorbar(sedlist['r_pc'], sedlist['mass'], yerr=sedlist['error_mass'],fmt='.b',ecolor='b')\n",
    "ax1.set_xlabel(r'Equivalent Radius (pc)',fontsize=20)\n",
    "ax1.set_ylabel('Mass ($M_\\odot$)',fontsize=20)\n",
    "ax1.text(0.27, 180, '$870 M_{\\odot}(r / \\mathrm{pc})^{1.33}$', size = 14, rotation = 35)\n",
    "for i in range(len(sedlist)):\n",
    "    if i+1==52:\n",
    "        ax1.annotate(i+1,xy = (sedlist['r_pc'][i],sedlist['mass'][i]),size=15)      \n",
    "    if sedlist['mass'][i]/(870*sedlist['r_pc'][i]**1.33) > 1:\n",
    "        ax1.annotate(i+1,xy = (sedlist['r_pc'][i],sedlist['mass'][i]),size=15)\n",
    "        \n",
    "fig.savefig('relationMR.eps', papertype='a2',\n",
    "                bbox_inches='tight')\n",
    "fig.savefig('relationMR.pdf', papertype='a2',\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]\n",
    "for i in range(len(sedlist[\"Nh2f\"])):\n",
    "    if sedlist[\"Nh2f\"][i]>7:\n",
    "        a.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sedlist = sedlist[a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "aList = []\n",
    "for i in range(len(sedlist)):\n",
    "    if sedlist['mass'][i]/(870*sedlist['r_pc'][i]**1.33) > 1:\n",
    "        aList.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14, 25, 41, 42]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.263"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(sedlist['r_pc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.238"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(sedlist['r_pc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(sedlist['mass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3558.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(sedlist['mass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;r_pc&apos; dtype=&apos;float64&apos; unit=&apos;pc&apos; length=56&gt;\n",
       "<table>\n",
       "<tr><td>0.547</td></tr>\n",
       "<tr><td>0.361</td></tr>\n",
       "<tr><td>0.506</td></tr>\n",
       "<tr><td>0.53</td></tr>\n",
       "<tr><td>0.856</td></tr>\n",
       "<tr><td>0.511</td></tr>\n",
       "<tr><td>0.661</td></tr>\n",
       "<tr><td>0.729</td></tr>\n",
       "<tr><td>0.597</td></tr>\n",
       "<tr><td>0.741</td></tr>\n",
       "<tr><td>0.425</td></tr>\n",
       "<tr><td>0.562</td></tr>\n",
       "<tr><td>...</td></tr>\n",
       "<tr><td>0.683</td></tr>\n",
       "<tr><td>0.415</td></tr>\n",
       "<tr><td>0.926</td></tr>\n",
       "<tr><td>0.7</td></tr>\n",
       "<tr><td>0.54</td></tr>\n",
       "<tr><td>0.726</td></tr>\n",
       "<tr><td>1.039</td></tr>\n",
       "<tr><td>0.742</td></tr>\n",
       "<tr><td>1.176</td></tr>\n",
       "<tr><td>0.572</td></tr>\n",
       "<tr><td>0.389</td></tr>\n",
       "<tr><td>0.577</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Column name='r_pc' dtype='float64' unit='pc' length=56>\n",
       "0.547\n",
       "0.361\n",
       "0.506\n",
       " 0.53\n",
       "0.856\n",
       "0.511\n",
       "0.661\n",
       "0.729\n",
       "0.597\n",
       "0.741\n",
       "0.425\n",
       "0.562\n",
       "  ...\n",
       "0.683\n",
       "0.415\n",
       "0.926\n",
       "  0.7\n",
       " 0.54\n",
       "0.726\n",
       "1.039\n",
       "0.742\n",
       "1.176\n",
       "0.572\n",
       "0.389\n",
       "0.577"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sedlist['r_pc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize = (8,8))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.set_xscale('log')\n",
    "ax1.set_yscale('log')\n",
    "ax1.set_xlim(0.2,1.3)\n",
    "ax1.set_ylim(50,4000)\n",
    "x = np.logspace(np.log10(0.002), np.log10(30), 1000)\n",
    "y = 870.0*x**1.33\n",
    "ax1.plot(x,y,'r--',zorder = 1, linewidth = 1)\n",
    "\"\"\"for i in range(len(sedlist)):\n",
    "    if (sedlist[\"Ma\"][i]/sedlist['mass'][i])<=2:       \n",
    "        ax1.scatter(sedlist['r_pc'][i],sedlist['mass'][i], marker = '.',s=60,  \n",
    "                    color = 'blue')\n",
    "    else:\n",
    "        ax1.scatter(sedlist['r_pc'][i],sedlist['mass'][i], marker = '.',s=60, \n",
    "                    color = 'red')\n",
    "\"\"\"\n",
    "#ax1.errorbar(sedlist['r_pc'], sedlist['mass'], yerr=sedlist['error_mass'],fmt='.b',ecolor='b')\n",
    "ax1.set_xlabel(r'Equivalent Radius (pc)',fontsize=20)\n",
    "ax1.set_ylabel('Mass ($M_\\odot$)',fontsize=20)\n",
    "ax1.text(0.27, 180, '$870 M_{\\odot}(r / \\mathrm{pc})^{1.33}$', size = 14, rotation = 35)\n",
    "for i in range(len(sedlist)):\n",
    "    ax1.scatter(sedlist['r_pc'][i],sedlist['mass'][i], marker = '.',s=60,  \n",
    "                    color = 'blue')\n",
    "    if sedlist['mass'][i]/(870*sedlist['r_pc'][i]**1.33) > 1:\n",
    "        ax1.annotate(i+1,xy = (sedlist['r_pc'][i],sedlist['mass'][i]),size=15)\n",
    "        \n",
    "fig.savefig('relationMR.eps', papertype='a2',\n",
    "                bbox_inches='tight')\n",
    "fig.savefig('relationMR.pdf', papertype='a2',\n",
    "                bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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